Instructions to use ND911/Franken-Mistral-Maid-TWK-Slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ND911/Franken-Mistral-Maid-TWK-Slerp with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ND911/Franken-Mistral-Maid-TWK-Slerp")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ND911/Franken-Mistral-Maid-TWK-Slerp")
model = AutoModelForCausalLM.from_pretrained("ND911/Franken-Mistral-Maid-TWK-Slerp")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ND911/Franken-Mistral-Maid-TWK-Slerp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ND911/Franken-Mistral-Maid-TWK-Slerp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ND911/Franken-Mistral-Maid-TWK-Slerp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ND911/Franken-Mistral-Maid-TWK-Slerp

SGLang

How to use ND911/Franken-Mistral-Maid-TWK-Slerp with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ND911/Franken-Mistral-Maid-TWK-Slerp" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ND911/Franken-Mistral-Maid-TWK-Slerp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ND911/Franken-Mistral-Maid-TWK-Slerp" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ND911/Franken-Mistral-Maid-TWK-Slerp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ND911/Franken-Mistral-Maid-TWK-Slerp with Docker Model Runner:
```
docker model run hf.co/ND911/Franken-Mistral-Maid-TWK-Slerp
```

Franken-Mistral-Maid-TWK-Slerp / model-00004-of-00008.safetensors

ND911

Upload 16 files

62736e3 verified over 2 years ago

Download

History Blame Contribute Delete

1.9 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 6aaa12753cf14b1832e2c0d1e6ce26b73850bc45477b1578c800fa3467a2b0b0
Size of remote file:: 1.9 GB
SHA256:: 580db40889b21a7ded2574d0070ac9829d0b521d7eae3d751a81b1e5dfa059e2

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.